Wu Xiaoya, Liang Chuang, Bustillo Juan, Kochunov Peter, Wen Xuyun, Sui Jing, Jiang Rongtao, Yang Xiao, Fu Zening, Zhang Daoqiang, Calhoun Vince D, Qi Shile
College of Artificial Intelligence, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
The Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing University of Aeronautics and Astronautics, Nanjing, China.
Hum Brain Mapp. 2025 Apr 1;46(5):e70206. doi: 10.1002/hbm.70206.
Neuropsychiatric disorders are associated with altered functional connectivity (FC); however, the reported regional patterns of functional alterations suffered from low replicability and high variability. This is partly because of differences in the atlas and delineation techniques used to measure FC-related deficits within/across disorders. We systematically investigated the impact of the brain parcellation approach on the FC-based brain network analysis. We focused on identifying the replicable FCs using three structural brain atlases, including Automated Anatomical Labeling (AAL), Brainnetome atlas (BNA) and HCP_MMP_1.0, and four functional brain parcellation approaches: Yeo-Networks (Yeo), Gordon parcel (Gordon) and two Schaefer parcelletions, among correlation, group difference, and classification tasks in six neuropsychiatric disorders: attention deficit and hyperactivity disorder (ADHD, n = 340), autism spectrum disorder (ASD, n = 513), schizophrenia (SZ, n = 200), schizoaffective disorder (SAD, n = 142), bipolar disorder (BP, n = 172), and major depression disorder (MDD, n = 282). Our cross-atlas/disorder analyses demonstrated that frontal-related FC deficits were reproducible in all disorders, independent of the atlasing approach; however, replicable FC extraction in other areas and the classification accuracy were affected by the parcellation schema. Overall, functional atlases with finer granularity performed better in classification tasks. Specifically, the Schaefer atlases generated the most repeatable FC deficit patterns across six illnesses. These results indicate that frontal-related FCs may serve as potential common and robust neuro-abnormalities across 6 psychiatric disorders. Furthermore, in order to improve the replicability of rsfMRI-based FC analyses, this study suggests the use of functional templates at larger granularity.
神经精神疾病与功能连接(FC)改变有关;然而,所报道的功能改变的区域模式存在低重复性和高变异性问题。部分原因在于用于测量疾病内/疾病间与FC相关缺陷的图谱和划定技术存在差异。我们系统地研究了脑部分割方法对基于FC的脑网络分析的影响。我们聚焦于使用三种结构性脑图谱(包括自动解剖标记(AAL)、脑网络组图谱(BNA)和HCP_MMP_1.0)以及四种功能性脑部分割方法(Yeo网络(Yeo)、戈登分割(Gordon)和两种谢弗分割)来识别可重复的FC,涉及六种神经精神疾病的相关性、组间差异和分类任务,这六种疾病分别是注意缺陷多动障碍(ADHD,n = 340)、自闭症谱系障碍(ASD,n = 513)、精神分裂症(SZ,n = 200)、分裂情感性障碍(SAD,n = 142)、双相情感障碍(BP,n = 172)和重度抑郁症(MDD,n = 282)。我们的跨图谱/疾病分析表明,与额叶相关的FC缺陷在所有疾病中都是可重复的,与图谱绘制方法无关;然而,其他区域可重复的FC提取和分类准确性受分割模式影响。总体而言,具有更细粒度的功能图谱在分类任务中表现更好。具体来说,谢弗图谱在六种疾病中产生了最可重复的FC缺陷模式。这些结果表明,与额叶相关的FC可能是六种精神疾病潜在的共同且稳健的神经异常特征。此外,为了提高基于静息态功能磁共振成像(rsfMRI)的FC分析的可重复性,本研究建议使用更大粒度的功能模板。